TimeXtender streamlines and speeds up data discovery

Today, artificial intelligence and other modern analytic techniques are radically changing how business people use information in decision making. We are in an exciting era of data democratization. Data scientists need timely access to data for modeling, and business users are increasingly requiring quicker access to more data needed to make better business decisions. When it comes to information, we are entering a new age of discovery where business must be enabled to extract more value, more easily and consistently, than ever before. Meanwhile, new privacy regulations, such as GDPR, require levels of data management and governance previously unseen. To provide unprecedented levels of access to data in a compliant manner, IT requires an integrated, automated delivery environment that spans all data sources.

Data enables AI, but integrating and automating access to timely, relevant, and reliable data is what allows AI to really shine. As TimeXtender CEO, Heine Krog Iversen, highlights, “I think the biggest issue that all companies are facing today is access to data. The number of data sources is growing rapidly every day. And the complexity of the use cases where you want to use data is also growing. In the old days, it was reporting; now, it's AI, machine learning, and predictive analytics.” This is why TimeXtender offers the Discovery Hub, which is a fully automated solution that aims to give every user, from data scientists to business users, in every company on the planet instant access to all relevant data.

Solving data-access challenges for AI

The Discovery Hub from TimeXtender addresses the challenges around data access by providing a platform to easily merge data from 100+ enterprise data sources using agile data modeling techniques to build a reliable operational data exchange (ODX) in a structured, governed, and secure environment, thus speeding up organizations’ digital transformations. The ODX serves as a central point for all data needs by providing appropriate access to IT teams, analytics teams and data scientists that require data for advanced AI modeling. This allows data scientists to obtain timely, compliant access to data and frees them from the laborious task of preparing data, so they can focus on valuable modeling activities.

As a platform that enables instant access to data, the automation under the hood of the Discovery Hub results in greater access, speed and agility with data. This allows organizations to simplify the process of preparing data for AI services such as Azure Machine Learning (ML), thereby reducing the time it takes to model and ultimately to operationalize the results.

And in operationalizing the results of the AI process, the Discovery Hub shines again. The Discovery Hub helps this operationalization by relating AI data to other business data and systems for maximum impact. Siloed data, such as the result set from an Azure ML model, cannot be leveraged to its fullest extent unless it can be combined with other business data. For example, a business could benefit from the results of its Azure ML predictive maintenance model alone, but the full benefit of those results is realized when that data is integrated more broadly. In this example, the results of the Azure ML predictive maintenance model, based on Azure IoT Hub data, can already be integrated into the ODX and related to data from a CRM system containing scheduled maintenance calls, ultimately helping the business to be better prepared and act faster. All of this can be done in the Discovery Hub while still providing a structured, governed, and documented enterprise analytical environment. Coupled with Power Platform as a visualization layer, the business now has the combined power of the Microsoft data and AI world at its fingertips.